Adaptive Classifiers-Ensemble System for Tracking Concept Drift

• Published in: 2007 International Conference on Machine Learning and Cybernetics Vol. 6; pp. 3607 - 3612
• Main Authors: Nishida, K., Yamauchi, K.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.01.2007
• Subjects: Adaptive systems; changing environments; Concept drift; Cybernetics; drift detection; Electronic mail; Information science; Machine learning; multiple classifiers system
• ISBN: 1424409721; 9781424409723
• ISSN: 2160-133X
• DOI: 10.1109/ICMLC.2007.4370772

Adapting to various types of concept drift is important for dealing with real-world online learning problems. To achieve this, we previously reported an online learning system that uses an ensemble of classifiers, the adaptive classifiers-ensemble (ACE) system. ACE consists of one online classifier, many batch classifiers, and a drift detection mechanism. To improve the performance of ACE, we have improved the weighting method, which combines the outputs of classifiers, and have added a new classifier pruning method. Experimental results showed that the enhanced ACE performed well for a synthetic dataset that contained both sudden and gradual changes and recurring concepts.